title: A new statistical approach to predict bacteremia using electronic medical records.
abstract: BACKGROUND
  Previous attempts to predict bacteremia have focused on selecting significant variables. However, these approaches have had limitations such as poor reproducibility in prediction accuracy and inconsistency in predictor selection. Here we propose a Bayesian approach to predict bacteremia based on the statistical distributions of clinical variables of previous patients, which has recently become possible through the adoption of electronic medical records.
  
  
  METHODS
  In a derivation cohort, Bayesian prediction models were derived and their discriminative performance was compared with previous models under varying combinations of predictors. Then the Bayesian models were prospectively tested in a validation cohort. According to Bayesian probabilities of bacteremia, patients in both cohorts were grouped into bacteremia risk groups.
  
  
  RESULTS
  Using the same prediction variables, the Bayesian predictions were more accurate than conventional rule-based predictions. Moreover, their better discriminative performance remained consistent despite variations in clinical variables. The receiver operating characteristic (ROC) area of the Bayesian model with 20 predictors was 0.70 ± 0.007 in the derivation cohort and 0.70 ± 0.018 in the validation cohort. The prevalence of bacteremia in groups I, II, and VI (grouped according to probability ratio) were 1.9%, 3.4%, and 20.0% in the derivation cohort, and 0.4%, 3.2%, and 18.4% in the validation cohort, respectively. The overall prevalence of bacteremia was 6.9% in both cohorts.
  
  
  CONCLUSIONS
  In the present study, the Bayesian prediction model showed stable performance in predicting bacteremia and identifying risk groups, as the previous models did. The clinical significance of the Bayesian approach is expected to be demonstrated through a multicenter trial.
authors.affiliation: Department of Internal Medicine, Yonsei University College of Medicine and Gangnam Severance Hospital, Seoul, Korea.
authors.forename: Sung Joon
authors.initials: SJ
authors.lastname: Jin
authors.forename: Mingoo
authors.initials: M
authors.lastname: Kim
authors.forename: Ji Hyun
authors.initials: JH
authors.lastname: Yoon
authors.forename: Young Goo
authors.initials: YG
authors.lastname: Song
mesh_terms: D000328:Adult; D000368:Aged; D016470:Bacteremia / Q000175:diagnosis* / Q000453:epidemiology*; D001499:Bayes Theorem; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011446:Prospective Studies; D012372:ROC Curve; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012307:Risk Factors